AI Semantic Copywriting Engine & E-E-A-T Quality Checker
An E-E-A-T checker and semantic copywriting engine evaluates web content against Google's Search Quality Rater Guidelines (Experience, Expertise, Authoritativeness, and Trustworthiness) while enforcing deterministic Algorithmic Authorship rules that eliminate generic AI tropes. In 2026, ranking in Google AI Overviews, Perplexity, and traditional organic search requires content structured with exact verb modality matching, 3-gram conversational target inclusion, and micro-semantic density. seoskillsai.com automates the entire Holistic Semantic 10-Phase Copywriting SOP across Anthropic Claude Code (MCP), Google Antigravity, OpenAI ChatGPT, and Cursor IDE.
⚡ Direct Execution Centerpiece: Autonomous E-E-A-T & Copywriting CLI
Evaluate any drafted markdown article or published URL for E-E-A-T compliance and algorithmic authorship integrity:
# Run Automated E-E-A-T & Stylometric Audit via Universal CLI
npx @seoskillsai/cli check-eeat --file="draft.md" --strict --modality-check
# Claude Code CLI Content Generation
claude mcp call seoskillsai write_semantic_article '{"keyword": "eeat checker", "word_count": 3500}'
# Google Antigravity Native Skill Invocation
/Semantic-seo-copywriter keyword="eeat checker" target_url="https://seoskillsai.com/skills/seo-content"
┌─────────────────────────────────────────────────────────────────────────────┐
│ Holistic Semantic ALGORITHMIC AUTHORSHIP & E-E-A-T COMPLIANCE MATRIX │
├────┬─────────────────────────────┬──────────────────────────────────────────┤
│ 01 │ Modality Matching │ First sentence matches query verb frame │
│ 02 │ 3-Gram Overlap │ Verbatim conversational n-grams in text │
│ 03 │ Entity Gap Injection │ Missing Wikipedia entities integrated │
│ 04 │ Edward's Formula │ Keyword only in Title, Slug, H1, Intro │
│ 05 │ Micro-Semantics Density │ Numeric values, dates, zero fluff adverbs│
│ 06 │ Data Hierarchy │ Direct answer placed above 600px fold │
│ 07 │ Anti-AI Stylometry │ Sentence length variation, no "Overall," │
│ 08 │ Trust Citations & Bylines │ Verified author credentials & references │
└────┴─────────────────────────────┴──────────────────────────────────────────┘
✍️ The 10 Core Rules of Algorithmic Authorship
graph TD
A["Query Analysis & Intent"] --> B["Rule 1: Modality Matching (Direct Answer Hero)"]
B --> C["Rule 2: Edward's Formula (Keyword Minimalism)"]
C --> D["Rule 3: Algorithmic Sentence Construction (Active Voice)"]
D --> E["Rule 4: Micro-Semantics & Concrete Metrics"]
E --> F["Rule 5: 3-Gram Conversational Target Injections"]
F --> G["Rule 6: Entity Gap Closure (Wikipedia Triples)"]
G --> H["Rule 7: Zero-Search-Volume N-gram Seeding"]
H --> I["Rule 8: Anti-AI Stylometric De-Templating"]
I --> J["Rule 9: Structured Data & Schema Binding"]
J --> K["Rule 10: E-E-A-T Reviewer Verification"]
1. Modality Matching
The opening sentence of every article MUST mirror the grammatical mood and verb modality of the search query:
- Query:
how to audit seo with ai $\rightarrow$ Opening: To audit SEO with AI, initialize a headless crawler...
- Query:
is claude better than ahrefs for seo $\rightarrow$ Opening: Claude executes real-time code modifications, whereas Ahrefs reports historical indexation data.
- Query:
best eeat checker tool $\rightarrow$ Opening: An E-E-A-T checker evaluates Experience, Expertise, Authoritativeness, and Trustworthiness...
2. Edward's Formula (Keyword Minimalism)
Over-optimizing keyword density triggers search spam penalties. Under Edward's Formula, the primary target keyword appears strictly in:
- Meta Title Tag
- URL Slug
- Main H1 Tag
- First Sentence Opening (within first 15 words)
- Meta Description
- First Image Alt Text
The remainder of the text uses semantic co-occurrences, entity attributes, and LSI synonyms.
3. Anti-AI Stylometry & Slop Elimination
Search engines easily detect templated AI output. Our engine strictly forbids:
- ❌ Opening paragraphs with "In today's fast-paced digital world..."
- ❌ Sentences beginning with "If you are looking to..."
- ❌ Section conclusions starting with "Overall,", "In summary,", or "In conclusion,"
- ❌ Excessive adverbs ("crucial", "vital", "game-changing", "seamlessly")
💻 Multi-Agent E-E-A-T Checker Script
Run this Python script to audit any markdown draft for E-E-A-T signals, forbidden words, and modality matching:
import re
FORBIDDEN_WORDS = [
"in conclusion", "overall,", "in summary", "fast-paced world",
"game-changer", "dive deep", "vital to note", "seamlessly"
]
def audit_eeat_content(markdown_text: str):
issues = []
# 1. Check for Forbidden Slop
for word in FORBIDDEN_WORDS:
matches = len(re.findall(r'\b' + re.escape(word) + r'\b', markdown_text, re.I))
if matches > 0:
issues.append(f"[!] Forbidden phrase detected ({matches}x): '{word}'")
# 2. Check Modality Match in First Paragraph
first_p = markdown_text.strip().split('\n\n')[1] if '\n\n' in markdown_text else ""
if first_p.startswith("If") or first_p.startswith("When"):
issues.append("[!] First paragraph violates Modality Matching (Starts with conditional clause).")
# 3. Check Sentence Length Variance
sentences = re.split(r'[.!?]+', markdown_text)
lengths = [len(s.split()) for s in sentences if len(s.split()) > 0]
avg_len = sum(lengths) / len(lengths) if lengths else 0
print(f"=== E-E-A-T & Stylometry Audit Report ===")
print(f"[✓] Average Sentence Length: {avg_len:.1f} words (Ideal: 12-18)")
print(f"[✓] Total Issues Detected: {len(issues)}")
for issue in issues:
print(f" {issue}")
if __name__ == "__main__":
sample = "An E-E-A-T checker evaluates search quality signals. It audits experience, expertise, authoritativeness, and trust across technical and editorial dimensions."
audit_eeat_content(sample)
❓ Frequently Asked Questions
How do I audit E-E-A-T quality signals automatically using AI?
Use npx @seoskillsai/cli check-eeat --file=article.md. The tool evaluates author credential markup, verifies primary source outbound citations, checks for medical/financial disclaimers, and flags unsubstantiated claims.
What are the rules of Algorithmic Authorship and Modality Matching?
Algorithmic authorship requires direct active voice, zero nested conditional clauses, specific numerical data points, and exact alignment between the query's verb frame and the document's direct opening answer.
How does Google evaluate experience, expertise, authoritativeness, and trust in AI content?
Google evaluates E-E-A-T through structured author schemas (linking to verified LinkedIn/Wikidata entities), first-hand proprietary data, original visual assets, and cross-reference citations in major knowledge graphs.
🔗 Connected Authority & Phase 1 Macro Pillars
